Tech Guides - Artificial Intelligence

In most conversations surrounding cybersecurity these days, the term “digital transformation,” gets frequently thrown in the mix, especially when the discussion revolves around AIOps. If you’ve got the slightest bit of interest in any recent developments in the cybersecurity world, you might have an idea of what AIOps is. However, if you didn’t already know- AIOps refers to a multi-layered, modern technology platform that allows enterprises to maximize IT operations by integrating AI and machine learning to detect and solve cybersecurity issues as they occur. As the name suggests, AIOps makes use of essential AI technology such as machine learning for the overall improvement of an organization’s IT operations. However, today- the role that AIOps plays has shifted dramatically- which leaves a lot of room for confusion to harbor amongst cybersecurity officers since most enterprises prefer to take the more conventional route as far as AI application is concerned. To utilize the most out of AIOps, enterprises need to understand the significance of the changes in the present-day IT environment, and how those changes influence the AI’s applications. To aid readers in understanding the volatility of the relationship between AI’s applications and the IT environment it is applicable in, we’ve put together an article that dives into the differences between conventional monitoring methods and present-day enterprise needs. Moreover, we’ll also be shining a light on the importance of the adoption of AIOps in enterprises as well. How has the IT environment changed in the modern times? Before we can get into every nook and cranny of why the transition from a traditional approach to a more modern approach matters, we’d like to make one thing very clear. Just because a specific approach works for one organization in no way guarantees that it would work for you. Perhaps the greatest advice any business owner could receive is to plan according to the specific requirements of their security and IT infrastructure. The greatest shortcoming of many CISOs and CSOs is that they fail to understand the particular needs of their IT environment and rely on conventional applications of AI to maximize the overall IT experience. Speaking of traditional AIOps applications, since the number of ‘moving’ parts or components involved was significantly less in number- the involvement of AI was far less complex, and therefore much easier to monitor and control. In a more modern setting, however, with the wave of digitalization and the ever-growing reliance that enterprises have on cloud computing systems, the number of components involved has increased, which also makes understanding the web much more difficult. Bearing witness to the ever-evolving and complex nature of today’s IT environment are the results of the research conducted by Dynatrace. The results explicitly state that something as simple as a single web or mobile application transaction can involve a staggering number of 37 different components or technologies on average. Taking this into account, relying on a traditional approach to AI becomes redundant, and ineffective since the conventional approach relies on an extremely limited understanding and fails to make sense of all the information provided by an arsenal of tools and dashboards. Not only is the conventional approach to AIOps impractical within the modern IT context, but it is also extremely outdated. Having said that perhaps the only approach that fits in the modern-day IT environment is a software intelligence-centric approach, which allows for fast-paced and robust solutions to present-day IT complexities. How important is AIOps for enterprises today? As we’ve already mentioned above, the present-day IT infrastructure requires a drastic change in the relationship that enterprises have had with AIOps so far. For starters, enterprises and organizations need to realize the importance of the role that AIOps plays. Unfortunately, however, there’s an overarching tendency seen in enterprises that enables them the naivety of labeling investing in AIOps as yet another “IT expense.” On the contrary, AIOps is essential for companies and organizations today, since every company is undergoing digitalization, along with increasing their reliance on modern technology more and more. Some cybersecurity specialists might even argue that each company is slowly turning into a software company, primarily because of the rise in cloud-computing systems. AIOps also works on improving the ‘business’ aspect of an enterprise, since the modern consumer looks for enterprises that offer innovative features, along with their ability to enhance user experience through an impeccable and seamless digital experience. Furthermore, in the competitive economic conditions of today, carrying out business operations in a timely manner is critical to an enterprise’s longevity- which is where the integration of AI can help an organization function smoothly. It should also be pointed out that the employment of AIOps opens up new avenues for businesses to step into since it removes the element of fear present in most business owners. The implementation of AIOps also enables an organization to make quick-paced releases since it takes IT problems out of the equation. These problems usually consist of bugs, regulation, and compliance, along with monitoring the overall IT experience being provided to consumers. How can enterprises ensure the longevity of their reliance on AIOps? When it comes to the integration of any new technology into an organization’s routine functions, there are always questions to be asked regarding the impact of the continued reliance on modern technology. To demonstrate the point we’ve made above, let’s return to a tech we’ve referred to throughout the article- cloud computing. Introduced in the 1960s, cloud computing revolutionized data storage to what it is today. However, after a couple of years and some unfortunate cyberattacks launched on cloud storage networks, cybersecurity specialists have found some dire problems with complete dependency on cloud computing storage. Similarly, many cybersecurity specialists and researchers wonder about the negative impacts that a dependency on AIOps could have in the future. When it comes to ensuring enterprises about the longevity of amalgamating AIOps into an enterprise, we’d like to give our assurance through the following reasons: Unlike cloud computing, developments in AIOps are heavily rooted in real-time data fed to the algorithm by an IT team. When you strip down all the fancy IT jargon, the only thing identity you need to trust is that of your IT personnel. Since AIOps relies on smart auto-remediation capabilities, business owners can see an immediate response geared by the employed algorithms. One such way that AIOps deploys auto-remediation strategies is by sending out alerts of any possible issue- the practice of which enables businesses to operate on the “business” side of the spectrum since they’ve got a trustworthy agent to rely on. Conclusion At the end of the article, we can only reinstate what’s been said before, in a thousand different ways- it’s high time that enterprises welcome change in the form of AIOps, instead of resisting it. In the modern age of digitalization, the key differences seen in the modern-day IT landscape should be reason enough for enterprises to be on the lookout for new alternatives to securing their data, and by extension- their companies. Author Bio Rebecca James is an enthusiastic cybersecurity journalist. A creative team leader, editor of PrivacyCrypts. What is AIOps and why is it going to be important? 8 ways Artificial Intelligence can improve DevOps Post-production activities for ensuring and enhancing IT reliability [Tutorial]

What if we could do chemistry inside a computer instead of in a test tube or beaker in the laboratory? What if running a new experiment was as simple as running an app and having it completed in a few seconds? For this to really work, we would want it to happen with complete fidelity. The atoms and molecules as modeled in the computer should behave exactly like they do in the test tube. The chemical reactions that happen in the physical world would have precise computational analogs. We would need a completely accurate simulation. If we could do this at scale, we might be able to compute the molecules we want and need. These might be for new materials for shampoos or even alloys for cars and airplanes. Perhaps we could more efficiently discover medicines that are customized to your exact physiology. Maybe we could get a better insight into how proteins fold, thereby understanding their function, and possibly creating custom enzymes to positively change our body chemistry. Is this plausible? We have massive supercomputers that can run all kinds of simulations. Can we model molecules in the above ways today?  This article is an excerpt from the book Dancing with Qubits written by Robert Sutor. Robert helps you understand how quantum computing works and delves into the math behind it with this quantum computing textbook.  Can supercomputers model chemical simulations? Let’s start with C8H10N4O2 – 1,3,7-Trimethylxanthine.  This is a very fancy name for a molecule that millions of people around the world enjoy every day: caffeine. An 8-ounce cup of coffee contains approximately 95 mg of caffeine, and this translates to roughly 2.95 × 10^20 molecules. Written out, this is 295, 000, 000, 000, 000, 000, 000 molecules. A 12 ounce can of a popular cola drink has 32 mg of caffeine, the diet version has 42 mg, and energy drinks often have about 77 mg. These numbers are large because we are counting physical objects in our universe, which we know is very big. Scientists estimate, for example, that there are between 10^49 and 10^50 atoms in our planet alone. To put these values in context, one thousand = 10^3, one million = 10^6, one billion = 10^9, and so on. A gigabyte of storage is one billion bytes, and a terabyte is 10^12 bytes. Getting back to the question I posed at the beginning of this section, can we model caffeine exactly on a computer? We don’t have to model the huge number of caffeine molecules in a cup of coffee, but can we fully represent a single molecule at a single instant? Caffeine is a small molecule and contains protons, neutrons, and electrons. In particular, if we just look at the energy configuration that determines the structure of the molecule and the bonds that hold it all together, the amount of information to describe this is staggering. In particular, the number of bits, the 0s and 1s, needed is approximately 10^48: 10, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000, 000. And this is just one molecule! Yet somehow nature manages to deal quite effectively with all this information. It handles the single caffeine molecule, to all those in your coffee, tea, or soft drink, to every other molecule that makes up you and the world around you. How does it do this? We don’t know! Of course, there are theories and these live at the intersection of physics and philosophy. However, we do not need to understand it fully to try to harness its capabilities.  We have no hope of providing enough traditional storage to hold this much information. Our dream of exact representation appears to be dashed. This is what Richard Feynman meant in his quote: “Nature isn’t classical.” However, 160 qubits (quantum bits) could hold 2^160 ≈ 1.46 × 10^48 bits while the qubits were involved in a computation. To be clear, I’m not saying how we would get all the data into those qubits and I’m also not saying how many more we would need to do something interesting with the information. It does give us hope, however. In the classical case, we will never fully represent the caffeine molecule. In the future, with enough very high-quality qubits in a powerful quantum computing system, we may be able to perform chemistry on a computer. How quantum computing is different than classical computing I can write a little app on a classical computer that can simulate a coin flip. This might be for my phone or laptop. Instead of heads or tails, let’s use 1 and 0. The routine, which I call R, starts with one of those values and randomly returns one or the other. That is, 50% of the time it returns 1 and 50% of the time it returns 0. We have no knowledge whatsoever of how R does what it does. When you see “R,” think “random.” This is called a “fair flip.” It is not weighted to slightly prefer one result over the other. Whether we can produce a truly random result on a classical computer is another question. Let’s assume our app is fair. If I apply R to 1, half the time I expect 1 and another half 0. The same is true if I apply R to 0. I’ll call these applications R(1) and R(0), respectively. If I look at the result of R(1) or R(0), there is no way to tell if I started with 1 or 0. This is just like a  secret coin flip where I can’t tell whether I began with heads or tails just by looking at how the coin has landed. By “secret coin flip,” I mean that someone else has flipped it and I can see the result, but I have no knowledge of the mechanics of the flip itself or the starting state of the coin.  If R(1) and R(0) are randomly 1 and 0, what happens when I apply R twice? I write this as R(R(1)) and R(R(0)). It’s the same answer: random result with an equal split. The same thing happens no matter how many times we apply R. The result is random, and we can’t reverse things to learn the initial value.  Now for the quantum version, Instead of R, I use H. It too returns 0 or 1 with equal chance, but it has two interesting properties. It is reversible. Though it produces a random 1 or 0 starting from either of them, we can always go back and see the value with which we began. It is its own reverse (or inverse) operation. Applying it two times in a row is the same as having done nothing at all.  There is a catch, though. You are not allowed to look at the result of what H does if you want to reverse its effect. If you apply H to 0 or 1, peek at the result, and apply H again to that, it is the same as if you had used R. If you observe what is going on in the quantum case at the wrong time, you are right back at strictly classical behavior.  To summarize using the coin language: if you flip a quantum coin and then don’t look at it, flipping it again will yield heads or tails with which you started. If you do look, you get classical randomness. A second area where quantum is different is in how we can work with simultaneous values. Your phone or laptop uses bytes as individual units of memory or storage. That’s where we get phrases like “megabyte,” which means one million bytes of information. A byte is further broken down into eight bits, which we’ve seen before. Each bit can be a 0 or 1. Doing the math, each byte can represent 2^8 = 256 different numbers composed of eight 0s or 1s, but it can only hold one value at a time. Eight qubits can represent all 256 values at the same time This is through superposition, but also through entanglement, the way we can tightly tie together the behavior of two or more qubits. This is what gives us the (literally) exponential growth in the amount of working memory. How quantum computing can help artificial intelligence Artificial intelligence and one of its subsets, machine learning, are extremely broad collections of data-driven techniques and models. They are used to help find patterns in information, learn from the information, and automatically perform more “intelligently.” They also give humans help and insight that might have been difficult to get otherwise. Here is a way to start thinking about how quantum computing might be applicable to large, complicated, computation-intensive systems of processes such as those found in AI and elsewhere. These three cases are in some sense the “small, medium, and large” ways quantum computing might complement classical techniques: There is a single mathematical computation somewhere in the middle of a software component that might be sped up via a quantum algorithm. There is a well-described component of a classical process that could be replaced with a quantum version. There is a way to avoid the use of some classical components entirely in the traditional method because of quantum, or the entire classical algorithm can be replaced by a much faster or more effective quantum alternative. As I write this, quantum computers are not “big data” machines. This means you cannot take millions of records of information and provide them as input to a quantum calculation. Instead, quantum may be able to help where the number of inputs is modest but the computations “blow up” as you start examining relationships or dependencies in the data.  In the future, however, quantum computers may be able to input, output, and process much more data. Even if it is just theoretical now, it makes sense to ask if there are quantum algorithms that can be useful in AI someday. To summarize, we explored how quantum computing works and different applications of artificial intelligence in quantum computing. Get this quantum computing book Dancing with Qubits by Robert Sutor today where he has explored the inner workings of quantum computing. The book entails some sophisticated mathematical exposition and is therefore best suited for those with a healthy interest in mathematics, physics, engineering, and computer science. Intel introduces cryogenic control chip, ‘Horse Ridge’ for commercially viable quantum computing Microsoft announces Azure Quantum, an open cloud ecosystem to learn and build scalable quantum solutions Amazon re:Invent 2019 Day One: AWS launches Braket, its new quantum service and releases

When people refer to artificial intelligence, some think of it as machine learning, while others think of it as deep learning or reinforcement learning, etc. While artificial intelligence is a broad term which involves machine learning, reinforcement learning is a type of machine learning, thereby a branch of AI. In this article we will understand 5 key reinforcement learning principles with some simple examples. Reinforcement learning allows machines and software agents to automatically determine the ideal behavior within a specific context, in order to maximize its performance. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. This article is an excerpt from the book AI Crash Course written by Hadelin de Ponteves. In this book Hadelin helps you understand what you really need to build AI systems with reinforcement learning. The book involves descriptive and practical projects to put ideas into action and show how to build intelligent software step by step. While reinforcement learning in some way is a form of AI, machine learning does not include the process of taking action and interacting with an environment like we humans do. Indeed, as intelligent human beings, what we constantly keep doing is the following: We observe some input, whether it's what we see with our eyes, what we hear with our ears, or what we remember in our memory. These inputs are then processed in our brain. Eventually, we make decisions and take actions. This process of interacting with an environment is what we are trying to reproduce in terms of artificial intelligence. And to that extent, the branch of AI that works on this is reinforcement learning. This is the closest match to the way we think; the most advanced form of artificial intelligence, if we see AI as the science that tries to mimic (or surpass) human intelligence. Reinforcement learning principles also has the most impressive results in business applications of AI. For example, Alibaba leveraged reinforcement learning to increase its ROI in online advertising by 240% without increasing their advertising budget. Five reinforcement learning principles Let's begin building the first pillars of your intuition into how reinforcement learning works. These are the fundamental reinforcement learning principles, which will get you started with the right, solid basics in AI. Here are the five principles: Principle #1: The input and output system Principle #2: The reward Principle #3: The AI environment Principle #4: The Markov decision process Principle #5: Training and inference Principle #1 – The input and output system The first step is to understand that today, all AI models are based on the common principle of input and output. Every single form of artificial intelligence, including machine learning models, chatBots, recommender systems, robots, and of course reinforcement learning models, will take something as input, and will return another thing as output. Figure 1: The input and output system In reinforcement learning, this input and output have a specific name: the input is called the state, or input state. The output is the action performed by the AI. And in the middle, we have nothing other than a function that takes a state as input and returns an action as output. That function is called a policy. Remember the name, "policy," because you will often see it in AI literature. As an example, consider a self-driving car. Try to imagine what the input and output would be in that case. The input would be what the embedded computer vision system sees, and the output would be the next move of the car: accelerate, slow down, turn left, turn right, or brake. Note that the output at any time (t) could very well be several actions performed at the same time. For instance, the self-driving car can accelerate while at the same time turning left. In the same way, the input at each time (t) can be composed of several elements: mainly the image observed by the computer vision system, but also some parameters of the car such as the current speed, the amount of gas remaining in the tank, and so on. That's the very first important principle in artificial intelligence: it is an intelligent system (a policy) that takes some elements as input, does its magic in the middle, and returns some actions to perform as output. Remember that the inputs are also called the states. Principle #2 – The reward Every AI has its performance measured by a reward system. There's nothing confusing about this; the reward is simply a metric that will tell the AI how well it does over time. The simplest example is a binary reward: 0 or 1. Imagine an AI that has to guess an outcome. If the guess is right, the reward will be 1, and if the guess is wrong, the reward will be 0. This could very well be the reward system defined for an AI; it really can be as simple as that! A reward doesn't have to be binary, however. It can be continuous. Consider the famous game of Breakout: Figure 2: The Breakout game Imagine an AI playing this game. Try to work out what the reward would be in that case. It could simply be the score; more precisely, the score would be the accumulated reward over time in one game, and the rewards could be defined as the derivative of that score. This is one of the many ways we could define a reward system for that game. Different AIs will have different reward structures; we will build five rewards systems for five different real-world applications in this book. With that in mind, remember this as well: the ultimate goal of the AI will always be to maximize the accumulated reward over time. Those are the first two basic, but fundamental, principles of artificial intelligence as it exists today; the input and output system, and the reward. Principle #3 – AI environment The third reinforcement learning principles involves an "AI environment." It is a very simple framework where you will define three things at each time (t): The input (the state) The output (the action) The reward (the performance metric) For each and every single AI based on reinforcement learning that is built today, we always define an environment composed of the preceding elements. It is, however, important to understand that there are more than these three elements in a given AI environment. For example, if you are building an AI to beat a car racing game, the environment will also contain the map and the gameplay of that game. Or, in the example of a self-driving car, the environment will also contain all the roads along which the AI is driving and the objects that surround those roads. But what you will always find in common when building any AI, are the three elements of state, action, and reward. Principle #4 – The Markov decision process The Markov decision process, or MDP, is simply a process that models how the AI interacts with the environment over time. The process starts at t = 0, and then, at each next iteration, meaning at t = 1, t = 2, … t = n units of time (where the unit can be anything, for example, 1 second), the AI follows the same format of transition: The AI observes the current state, st The AI performs the action, at The AI receives the reward, rt = R(st,at) The AI enters the following state, st+1 The goal of the AI is always the same in reinforcement learning: it is to maximize the accumulated rewards over time, that is, the sum of all the rt = R(st,at) received at each transition. received at each transition. The following graphic will help you visualize and remember an MDP better, the basis of reinforcement learning models: Figure 3: The Markov Decision process Now four essential pillars are already shaping your intuition of AI. Adding a last important one completes the foundation of your understanding of AI. The last principle is training and inference; in training, the AI learns, and in inference, it predicts. Principle #5 – Training and inference The final principle you must understand is the difference between training and inference. When building an AI, there is a time for the training mode, and a separate time for the inference mode. I'll explain what that means starting with the training mode. Training mode Now you understand, from the three first principles, that the very first step of building an AI is to build an environment in which the input states, the output actions, and a system of rewards are clearly defined. From the fourth principle, you also understand that inside this environment an AI will be built that interacts with it, trying to maximize the total reward accumulated over time. To put it simply, there will be a preliminary (and long) period during which the AI will be trained to do that. That period is called the training; we can also say that the AI is in training mode. During that time, the AI tries to accomplish a certain goal repeatedly until it succeeds. After each attempt, the parameters of the AI model are modified in order to do better at the next attempt. Inference mode Inference mode simply comes after your AI is fully trained and ready to perform well. It will simply consist of interacting with the environment by performing the actions to accomplish the goal the AI was trained to achieve before in training mode. In inference mode, no parameters are modified at the end of each episode. For example, imagine you have an AI company that builds customized AI solutions for businesses, and one of your clients asked you to build an AI to optimize the flows in a smart grid. First, you'd enter an R&D phase during which you would train your AI to optimize these flows (training mode), and as soon as you reached a good level of performance, you'd deliver your AI to your client and go into production. Your AI would regulate the flows in the smart grid only by observing the current states of the grid and performing the actions it has been trained to do. That's inference mode. Sometimes, the environment is subject to change, in which case you must alternate fast between training and inference modes so that your AI can adapt to the new changes in the environment. An even better solution is to train your AI model every day and go into inference mode with the most recently trained model. That was the last fundamental principle common to every AI. To summarize, we explored the five key reinforcement learning principles which involves the input and output system, a reward system, AI environment, Markov decision process, training and inference mode for AI. Get this guide AI Crash Course by Hadelin de Ponteves today to learn about programming an AI software in Python without any math or data science background. It will also help you master the key skills of deep learning, reinforcement learning, and deep reinforcement learning. How artificial intelligence and machine learning can help us tackle the climate change emergency DeepMind introduces OpenSpiel, a reinforcement learning-based framework for video games OpenAI’s AI robot hand learns to solve a Rubik Cube using Reinforcement learning and Automatic Domain Randomization (ADR) DeepMind’s AI uses reinforcement learning to defeat humans in multiplayer games

Until a couple of years ago, people barely knew the term 'data science' which has now evolved into an extremely popular career field. The Harvard Business Review dubbed data scientist within the data science team as the sexiest job of the 21st century and expert professionals jumped on the data is the new oil bandwagon. As per the Figure Eight Report 2018, which takes the pulse of the data science community in the US, a lot has changed rapidly in the data science field over the years. For the 2018 report, they surveyed approximately 240 data scientists and found out that machine learning projects have multiplied and more and more data is required to power them. Data science and machine learning jobs are LinkedIn's fastest growing jobs. And the internet is creating 2.5 quintillion bytes of data to process and analyze each day. With all these changes, it is evident for data science teams to evolve and change among various organizations. The data science team is responsible for delivering complex projects where system analysis, software engineering, data engineering, and data science is used to deliver the final solution. To achieve all of this, the team does not only have a data scientist or a data analyst but also includes other roles like business analyst, data engineer or architect, and chief data officer. In this post, we will differentiate and discuss various job roles within a data science team, skill sets required and the compensation benefit for each one of them. For an in-depth understanding of data science teams, read the book, Managing Data Science by Kirill Dubovikov, which has interesting case studies on building successful data science teams. He also explores how the team can efficiently manage data science projects through the use of DevOps and ModelOps.  Now let's get into understanding individual data science roles and functions, but before that we take a look at the structure of the team.There are three basic team structures to match different stages of AI/ML adoption: IT centric team structure At times for companies hiring a data science team is not an option, and they have to leverage in-house talent. During such situations, they take advantage of the fully functional in-house IT department. The IT team manages functions like data preparation, training models, creating user interfaces, and model deployment within the corporate IT infrastructure. This approach is fairly limited, but it is made practical by MLaaS solutions. Environments like Microsoft Azure or Amazon Web Services (AWS) are equipped with approachable user interfaces to clean datasets, train models, evaluate them, and deploy. Microsoft Azure, for instance, supports its users with detailed documentation for a low entry threshold. The documentation helps in fast training and early deployment of models even without an expert data scientists on board. Integrated team structure Within the integrated structure, companies have a data science team which focuses on dataset preparation and model training, while IT specialists take charge of the interfaces and infrastructure for model deployment. Combining machine learning expertise with IT resource is the most viable option for constant and scalable machine learning operations. Unlike the IT centric approach, the integrated method requires having an experienced data scientist within the team. This approach ensures better operational flexibility in terms of available techniques. Additionally, the team leverages deeper understanding of machine learning tools and libraries – like TensorFlow or Theano which are specifically for researchers and data science experts. Specialized data science team Companies can also have an independent data science department to build an all-encompassing machine learning applications and frameworks. This approach entails the highest cost. All operations, from data cleaning and model training to building front-end interfaces, are handled by a dedicated data science team. It doesn't necessarily mean that all team members should have a data science background, but they should have technology background with certain service management skills. A specialized structure model aids in addressing complex data science tasks that include research, use of multiple ML models tailored to various aspects of decision-making, or multiple ML backed services. Today's most successful Silicon Valley tech operates with specialized data science teams. Additionally they are custom-built and wired for specific tasks to achieve different business goals. For example, the team structure at Airbnb is one of the most interesting use cases. Martin Daniel, a data scientist at Airbnb in this talk explains how the team emphasizes on having an experimentation-centric culture and apply machine learning rigorously to address unique product challenges. Job roles and responsibilities within data science team As discussed earlier, there are many roles within a data science team. As per Michael Hochster, Director of Data Science at Stitch Fix, there are two types of data scientists: Type A and Type B. Type A stands for analysis. Individuals involved in Type A are statisticians that make sense of data without necessarily having strong programming knowledge. Type A data scientists perform data cleaning, forecasting, modeling, visualization, etc. Type B stands for building. These individuals use data in production. They're good software engineers with strong programming knowledge and statistics background. They build recommendation systems, personalization use cases, etc. Though it is rare that one expert will fit into a single category. But understanding these data science functions can help make sense of the roles described further. Chief data officer/Chief analytics officer The chief data officer (CDO) role has been taking organizations by storm. A recent NewVantage Partners' Big Data Executive Survey 2018 found that 62.5% of Fortune 1000 business and technology decision-makers said their organization appointed a chief data officer. The role of chief data officer involves overseeing a range of data-related functions that may include data management, ensuring data quality and creating data strategy. He or she may also be responsible for data analytics and business intelligence, the process of drawing valuable insights from data. Even though chief data officer and chief analytics officer (CAO) are two distinct roles, it is often handled by the same person. Expert professionals and leaders in analytics also own the data strategy and how a company should treat its data. It does make sense as analytics provide insights and value to the data. Hence, with a CDO+CAO combination companies can take advantage of a good data strategy and proper data management without losing on quality. According to compensation analysis from PayScale, the median chief data officer salary is $177,405 per year, including bonuses and profit share, ranging from $118,427 to $313,791 annually. Skill sets required: Data science and analytics, programming skills, domain expertise, leadership and visionary abilities are required. Data analyst The data analyst role implies proper data collection and interpretation activities. The person in this job role will ensure that collected data is relevant and exhaustive while also interpreting the results of the data analysis. Some companies also require data analysts to have visualization skills to convert alienating numbers into tangible insights through graphics. As per Indeed, the average salary for a data analyst is $68,195 per year in the United States. Skill sets required: Programming languages like R, Python, JavaScript, C/C++, SQL. With this critical thinking, data visualization and presentation skills will be good to have. Data scientist Data scientists are data experts who have the technical skills to solve complex problems and the curiosity to explore what problems are needed to be solved. A data scientist is an individual who develops machine learning models to make predictions and is well versed in algorithm development and computer science. This person will also know the complete lifecycle of the model development. A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyze customer and market trends. Basic responsibilities include gathering and analyzing data, using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets. According to Glassdoor, the current U.S. average salary for a data scientist is $118,709. Skills set required: A data scientist will require knowledge of big data platforms and tools like  Seahorse powered by Apache Spark, JupyterLab, TensorFlow and MapReduce; and programming languages that include SQL, Python, Scala and Perl; and statistical computing languages, such as R. They should also have cloud computing capabilities and knowledge of various cloud platforms like AWS, Microsoft Azure etc.You can also read this post on how to ace a data science interview to know more. Machine learning engineer At times a data scientist is confused with machine learning engineers, but a machine learning engineer is a distinct role that involves different responsibilities. A machine learning engineer is someone who is responsible for combining software engineering and machine modeling skills. This person determines which model to use and what data should be used for each model. Probability and statistics are also their forte. Everything that goes into training, monitoring, and maintaining a model is the ML engineer's job. The average machine learning engineer's salary is $146,085 in the US, and is ranked No.1 on the Indeed's Best Jobs in 2019 list. Skill sets required: Machine learning engineers will be required to have expertise in computer science and programming languages like R, Python, Scala, Java etc. They would also be required to have probability techniques, data modelling and evaluation techniques. Data architects and data engineers The data architects and data engineers work in tandem to conceptualize, visualize, and build an enterprise data management framework. The data architect visualizes the complete framework to create a blueprint, which the data engineer can use to build a digital framework. The data engineering role has recently evolved from the traditional software-engineering field.  Recent enterprise data management experiments indicate that the data-focused software engineers are needed to work along with the data architects to build a strong data architecture. Average salary for a data architect in the US ranges from $1,22,000 to $1,29, 000 annually as per a recent LinkedIn survey. Skill sets required: A data architect or an engineer should have a keen interest and experience in programming languages frameworks like HTML5, RESTful services, Spark, Python, Hive, Kafka, and CSS etc. They should have the required knowledge and experience to handle database technologies such as PostgreSQL, MapReduce and MongoDB and visualization platforms such as; Tableau, Spotfire etc. Business analyst A business analyst (BA) basically handles Chief analytics officer's role but on the operational level. This implies converting business expectations into data analysis. If your core data scientist lacks domain expertise, a business analyst can bridge the gap. They are responsible for using data analytics to assess processes, determine requirements and deliver data-driven recommendations and reports to executives and stakeholders. BAs engage with business leaders and users to understand how data-driven changes will be implemented to processes, products, services, software and hardware. They further articulate these ideas and balance them against technologically feasible and financially reasonable. The average salary for a business analyst is $75,078 per year in the United States, as per Indeed. Skill sets required: Excellent domain and industry expertise will be required. With this good communication as well as data visualization skills and knowledge of business intelligence tools will be good to have. Data visualization engineer This specific role is not present in each of the data science teams as some of the responsibilities are realized by either a data analyst or a data architect. Hence, this role is only necessary for a specialized data science model. The role of a data visualization engineer involves having a solid understanding of UI development to create custom data visualization elements for your stakeholders. Regardless of the technology, successful data visualization engineers have to understand principles of design, both graphical and more generally user-centered design. As per Payscale, the average salary for a data visualization engineer is $98,264. Skill sets required: A data visualization engineer need to have rigorous knowledge of data visualization methods and be able to produce various charts and graphs to represent data. Additionally they must understand the fundamentals of design principles and visual display of information. To sum it up, a data science team has evolved to create a number of job roles and opportunities, but companies still face challenges in building up the team from scratch and find it hard to figure where to start from. If you are facing a similar dilemma, check out this book, Managing Data Science, written by Kirill Dubovikov. It covers concepts and methodologies to manage and deliver top-notch data science solutions, while also providing guidance on hiring, growing and sustaining a successful data science team. How to learn data science: from data mining to machine learning How to ace a data science interview Data science vs. machine learning: understanding the difference and what it means today 30 common data science terms explained 9 Data Science Myths Debunked

Deep learning is one of the revolutionary breakthroughs of the decade for enterprise application development. Today, majority of organizations and enterprises have to transform their applications to exploit the capabilities of deep learning. In this article, we will discuss how to leverage the capabilities of JVM (Java virtual machine) to build deep learning applications. Entreprises prefer JVM Major JVM languages used in enterprise are Java, Scala, Groovy and Kotlin. Java is the most widely used programming language in the world. Nearly all major enterprises in the world use Java in some way or the other. Enterprises use JVM based languages such as Java to build complex applications because JVM features are optimal for production applications. JVM applications are also significantly faster and require much fewer resources to run compared to their counterparts such as Python. Java can perform more computational operations per second compared to Python. Here is an interesting performance benchmarking for the same. JVM optimizes performance benchmarks Production applications represent a business and are very sensitive to performance degradation, latency, and other disruptions. Application performance is estimated from latency/throughput measures. Memory overload and high resource usage can influence above said measures. Applications that demand more resources or memory require good hardware and further optimization from the application itself. JVM helps in optimizing performance benchmarks and tune the application to the hardware’s fullest capabilities. JVM can also help in avoiding memory footprints in the application. We have discussed on JVM features so far, but there’s an important context on why there’s a huge demand for JVM based deep learning in production. We’re going to discuss that next. Python is undoubtedly the leading programming language used in deep learning applications. For the same reason, the majority of enterprise developers i.e, Java developers are forced to switch to a technology stack that they’re less familiar with. On top of that, they need to address compatibility issues and deployment in a production environment while integrating neural network models. DeepLearning4J, deep learning library for JVM Java Developers working on enterprise applications would want to exploit deployment tools like Maven or Gradle for hassle-free deployments. So, there’s a demand for a JVM based deep learning library to simplify the whole process. Although there are multiple deep learning libraries that serve the purpose, DL4J (Deeplearning4J) is one of the top choices. DL4J is a deep learning library for JVM and is among the most popular repositories on GitHub. DL4J, developed by the Skymind team, is the first open-source deep learning library that is commercially supported. What makes it so special is that it is backed by ND4J (N-Dimensional Arrays for Java) and JavaCPP. ND4J is a scientific computational library developed by the Skymind team. It acts as the required backend dependency for all neural network computations in DL4J. ND4J is much faster in computations than NumPy. JavaCPP acts as a bridge between Java and native C++ libraries. ND4J internally depends on JavaCPP to run native C++ libraries. DL4J also has a dedicated ETL component called DataVec. DataVec helps to transform the data into a format that a neural network can understand. Data analysis can be done using DataVec just like Pandas, a popular Python data analysis library. Also, DL4J uses Arbiter component for hyperparameter optimization. Arbiter finds the best configuration to obtain good model scores by performing random/grid search using the hyperparameter values defined in a search space. Why choose DL4J for your deep learning applications? DL4J is a good choice for developing distributed deep learning applications. It can leverage the capabilities of Apache Spark and Hadoop to develop high performing distributed deep learning applications. Its performance is equivalent to Caffe in case multi-GPU hardware is used. We can use DL4J to develop multi-layer perceptrons, convolutional neural networks, recurrent neural networks, and autoencoders. There are a number of hyperparameters that can be adjusted to further optimize the neural network training. The Skymind team did a good job in explaining the important basics of DL4J on their website. On top of that, they also have a gitter channel to discuss or report bugs straight to their developers. If you are keen on exploring reinforcement learning further, then there’s a dedicated library called RL4J (Reinforcement Learning for Java) developed by Skymind. It can already play doom game! DL4J combines all the above-mentioned components (DataVec, ND4J, Arbiter and RL4J) for the deep learning workflow thus forming a powerful software suite. Most importantly, DL4J enables productionization of deep learning applications for the business. If you are interested to learn how to develop real-time applications on DL4J, checkout my new book Java Deep Learning Cookbook. In this book, I show you how to install and configure Deeplearning4j to implement deep learning models. You can also explore recipes for training and fine-tuning your neural network models using Java. By the end of this book, you’ll have a clear understanding of how you can use Deeplearning4j to build robust deep learning applications in Java. Author Bio Rahul Raj has more than 7 years of IT industry experience in software development, business analysis, client communication and consulting for medium/large scale projects. He has extensive experience in development activities comprising requirement analysis, design, coding, implementation, code review, testing, user training, and enhancements. He has written a number of articles about neural networks in Java and is featured by DL4J and Official Java community channel. You can follow Rahul on Twitter, LinkedIn, and GitHub. Top 6 Java Machine Learning/Deep Learning frameworks you can’t miss 6 most commonly used Java Machine learning libraries Deeplearning4J 1.0.0-beta4 released with full multi-datatype support, new attention layers, and more!

Cybersecurity analysts have warned that spoofing using artificial intelligence is within the realm of possibility and that people should be aware of the possibility of getting fooled with such voice or picture-based deepfakes. What is Deepfake? Deepfakes rely on a branch of AI called Generative Adversarial Networks (GANs). It requires two machine learning networks that teach each other with an ongoing feedback loop. The first one takes real content and alters it. Then, the second machine learning network, known as the discriminator, tests the authenticity of the changes. As the machine learning networks keep passing the material back and forth and receiving feedback about it, they get smarter. GANs are still in the early stages, but people expect numerous potential commercial applications. For example, some can convert a single image into different poses. Others can suggest outfits similar to what a celebrity wears in a photo or turn a low-quality picture into a high-resolution snapshot. But, outside of those helpful uses, deepfakes could have sinister purposes. Consider the blowback if a criminal creates a deepfake video of something that would hurt someone's reputation — for instance, a deepfake video of a politician "admitting" to illegal activities, like accepting a bribe. Other instances of this kind of AI that are already possible include cases of misleading spoken dialogue. Then, the lips of someone saying something offensive get placed onto someone else. In one of the best-known examples of Deepfake manipulation, BuzzFeed published a clip now widely known as "ObamaPeele." It combined a video of President Obama with film director Jordan Peele's lips. The result made it seem as if Obama cursed and said things he never would in public. Deepfakes are real enough to cause action The advanced deepfake efforts that cybersecurity analysts warn about rely on AI to create something so real that it causes people to act. For example, in March of 2019, the CEO of a British energy firm received a call from what sounded like his boss. The message was urgent — the executive needed to transfer a large amount of funds to a Hungarian supplier within the hour. Only after the money was sent did it become clear the executive’s boss was never on the line. Instead, cybercriminals had used AI to generate an audio clip that mimicked his boss’s voice. The criminals called the British man and played the clip, convincing him to transfer the funds. The unnamed victim was scammed out of €220,000 — an amount equal to $243,000. Reports indicate it's the first successful hack of its kind, although it's an unusual way for hackers to go about fooling victims. Some analysts point out other hacks like this may have happened but have gone unreported, or perhaps the people involved did not know hackers used this technology. According to Rüdiger Kirsch, a fraud expert at the insurance company that covered the full amount of the claim, this is the first time the insurer dealt with such an instance. The AI technology apparently used to mimic the voice was so authentic that it captured the parent company leader's German accent and the melody of his voice. Deepfakes capitalize on urgency One of the telltale signs of deepfakes and other kinds of spoofing — most of which currently happen online — is a false sense of urgency. For example, lottery scammers emphasize that their victims must send personal details immediately to avoid missing out on their prizes. The deepfake hackers used time constraints to fool this CEO, as well. The AI technology on the other end of the phone told the CEO that he needed to send the money to a Hungarian supplier within the hour, and he complied. Even more frighteningly, the deceiving tech was so advanced that hackers used it for several phone calls to the victim. One of the best ways to avoid scams is to get further verification from outside sources, rather than immediately responding to the person engaging with you. For example, if you're at work and get a call or email from someone in accounting who asks for your Social Security number or bank account details to update their records, the safest thing to do is to contact the accounting department yourself and verify the legitimacy. Many online spoofing attempts have spelling or grammatical errors, too. The challenging thing about voice trickery, though, is that those characteristics don't apply. You can only go by what your ears tell you. Since these kinds of attacks are not yet widespread, the safest thing to do for avoiding disastrous consequences is to ignore the urgency and take the time you need to verify the requests through other sources. Hackers can target deepfake victims indefinitely One of the most impressive things about this AI deepfake case is that it involved more than one phone conversation. The criminals called again after receiving the funds to say that the parent company had sent reimbursement funds to the United Kingdom firm. But, they didn't stop there. The CEO received a third call that impersonated the parent company representative again and requested another payment. That time, though, the CEO became suspicious and didn't agree. As, the promised reimbursed funds had not yet come through. Moreover, the latest call requesting funds originated from an Austrian phone number. Eventually, the CEO called his boss and discovered the fakery by handling calls from both the real person and the imposter simultaneously. Evidence suggests the hackers used commercially available voice generation software to pull off their attack. However, it is not clear if the hackers used bots to respond when the victim asked questions of the caller posing as the parent company representative. Why do deepfakes work so well? This deepfake is undoubtedly more involved than the emails hackers send out in bulk, hoping to fool some unsuspecting victims. Even those that use company logos, fonts and familiar phrases are arguably not as realistic as something that mimics a person's voice so well that the victim can't distinguish the fake from the real thing. The novelty of these incidents also makes individuals less aware that they could happen. Although many people receive training that helps them spot some online scams, the curriculum does not yet extend to these advanced deepfake cases. Making the caller someone in a position of power increases the likelihood of compliance, too. Generally, if a person hears a voice on the other end of the phone that they recognize as their superior, they won't question it. Plus, they might worry that any delays in fulfilling the caller's request might get perceived as them showing a lack of trust in their boss or an unwillingness to follow orders. You've probably heard people say, "I'll believe it when I see it." But, thanks to this emerging deepfake technology, you can't necessarily confirm the authenticity of something by hearing or seeing it. That's an unfortunate development, plus something that highlights how important it is to investigate further before acting. That may mean checking facts or sources or getting in touch with superiors directly to verify what they want you to do. Indeed, those extra steps take more time. But, they could save you from getting fooled. Author Bio Kayla Matthews writes about big data, cybersecurity, and technology. You can find her work on The Week, Information Age, KDnuggets and CloudTweaks, or over at ProductivityBytes.com. Media manipulation by Deepfakes and cheap fakes require both AI and social fixes, finds a Data & Society report Terrifyingly realistic Deepfake video of Bill Hader transforming into Tom Cruise is going viral on YouTube Now there is a Deepfake that can animate your face with just your voice and a picture using Temporal GANs

Artificial intelligence is a hot topic for many industries. When it comes to sales, the situation gets complicated. According to the latest Salesforce State of Sales report, just 21% of organizations use AI in sales today, while its adoption in sales is expected to grow 155% by 2020. Let’s explore what keeps sales teams from implementing AI and how to overcome these challenges to unlock new opportunities. Why do so few teams adopt AI in Sales There are a few reasons behind such a low rate of AI application in sales. First, some teams don’t feel they are prepared to integrate AI into their existing strategies. Second, AI technologies are often applied in a hectic way: many businesses have high expectations of AI and concentrate mostly on its benefits rather than contemplating possible difficulties upfront. Such an approach rarely results in positive business transformation. Here are some common challenges that businesses need to overcome to turn their sales AI projects into success stories. Businesses don’t know how to apply AI in their workflow Problem: Different industries call for different uses of AI. Still, companies tend to buy AI platforms to use them for the same few popular tasks, like predictions based on historical data or automatic data logging. In reality, the business type and direction should dictate what AI solution will best fit the needs of an organization. For example, in e-commerce, AI can serve dynamic product recommendations on the basis of the customer’s previous purchases or views. Teams relying on email marketing can use AI to serve personalized email content as well as optimize send times. Solution: Let a sales team participate in AI onboarding. Prior to setup, gain insight into your sales reps’ daily routine, needs, and pains. Then, get their feedback continuously during the actual AI implementation. Such a strategy will ensure the sales team benefits from a tailored, rather than a generic, AI system. AI requires data businesses don’t have Problem: AI is most efficient when fed with huge amounts of data. It’s true, a company with a few hundred leads per week will train AI for better predictions than the company with the same amount of leads per month. Frequently, companies assume they don’t have so much data or they cannot present it in a suitable format to train an AI algorithm. Solution: In reality, AI can be trained with incomplete and imperfect data. Instead of trying to integrate the whole set of data prior to implementing AI, it’s possible to use it with data subsets, like historical purchase data or promotional campaign analytics. Plus, AI can improve the quality of data by predicting missing elements or identifying possible errors. Businesses lack skills to manage AI platforms Problem: AI is a sophisticated algorithm that requires special skills to implement and use it. Thus, sales teams need to be augmented with specialized knowledge in data management, software optimization, and integration. Otherwise, AI tools can be used incorrectly and thus provide little value. Solution: There are two ways of solving this problem. First, it’s possible to create a new team of big data, machine learning, and analytics experts to run AI implementation and coordinate it with the sales team. This option is rather time-consuming. Second, it’s possible to buy an AI-driven platform, like Salesforce, for example, that includes both out-of-the-box features as well as plenty of customization opportunities. Instead of hiring new specialists to manage the platform, you can reach out to Salesforce consultants who will help you select the best-fit plan, configure, and implement it. If your requirements go beyond the features available by default, then it’s possible to add custom functionality. How AI can change the sales of tomorrow When you have a clear vision of the AI implementation challenges and understand how to overcome them, it’s time to make use of AI-provided benefits. A core benefit of any AI system is its ability to analyze large amounts of data across multiple platforms and then connect the dots, i.e. draw actionable conclusions. To illustrate these AI opportunities, let’s take Salesforce, one of the most popular solutions in this domain today, and see how its AI technology, Einstein, can enhance a sales workflow. Time-saving and productivity boost Administrative work eats up sales reps’ time that they can spend selling. That’s why many administrative tasks should be automated. Salesforce Einstein can save time usually wasted on manual data entry by: Automating contact creation and update Activity logging Generating lead status reports Syncing emails and calendars Scheduling meetings Efficient lead management When it comes to leads, sales reps tend to base their lead management strategies on gut feeling. In spite of its importance, intuition cannot be the only means of assessing leads. The approach should be more holistic. AI has unmatched abilities to analyze large amounts of information from different sources to help score and prioritize leads. In combination with sales reps’ intuition, such data can bring lead management to a new level. For example, Einstein AI can help with: Scoring leads based on historical data and performance metrics of the best customers Classifying opportunities in terms of their readiness to convert Tracking reengaged opportunities and nurturing them Predictive forecasting AI is well-known for its predictive capabilities that help sales teams make smarter decisions without running endless what-if scenarios. AI forecasting builds sales models using historical data. Such models anticipate possible outcomes of multiple scenarios common in sales reps’ work. Salesforce Einstein, for example, can give the following predictions: Prospects most likely to convert Deals most likely to close Prospects or deals to target New leads Opportunities to upsell or cross-sell The same algorithm can be used for forecasting sales team performance during a specified period of time and taking proactive steps based on those predictions. What’s more, sales intelligence is shifting from predictive to prescriptive, where prescriptive AI does not recommend but prescribes exact actions to be taken by sales reps to achieve a particular outcome. Watching out for pitfalls of AI in sales While AI promises to fulfil sales reps’ advanced requests, there are still some fears and doubts around it. First of all, as a rising technology, AI still carries ethical issues related to its safe and legitimate use in the workplace, such as those of the integrity of autonomous AI-driven decisions and legitimate origin of data fed to algorithms. While the full-fledged legal framework is yet to be worked out, governments have already stepped in. For example, the High-Level Expert Group on AI of the European Commission came up with the Ethics Guidelines for Trustworthy Artificial Intelligence covering every aspect from human oversight and technical robustness to data privacy and non-discrimination. In particular, non-discrimination relates to potential bias,, such as algorithmic bias that comes from human bias when sourcing data, and the one where correlation does not equal causation. Thus, AI-driven analysis should be incorporated in decision-making cautiously as just one of the many sources of insights. AI won’t replace a human mind⁠—the data still needs to be processed critically. When it comes to sales, another common concern is that AI will take sales reps’ jobs. Yes, some tasks that are deemed monotonous and time-consuming are indeed taken over by AI automation. However, it is actually a blessing as AI does not replace jobs but augments them. This way, sales reps can have more time on their hands to complete more creative and critical tasks. It's true, however, that employers would need people who know how to work with AI technologies. It means either ongoing training or new hires, which can be rather costly. The stakes are high, though. To keep up with the fast-changing world, one has to bargain their way to success, finding one’s way around current limitations and challenges. In a nutshell AI is key to boosting sales team performance. However, successful AI integration into sales and marketing strategies requires teams to overcome challenges posed by sophisticated AI technologies. Such popular AI-driven platforms like Salesforce help sales reps get hold of the AI potential as well as enjoy vast opportunities for saving time and increasing productivity. Author Bio Valerie Nechay is MarTech and CX Observer at Iflexion, a Denver-based custom software development provider. Using her writing powers, she's translating complex technologies into fascinating topics and shares them with the world. Now her focus is on Salesforce implementation how-tos, challenges, insights, and shortcuts, as well as broader applications of enterprise tech for business development. IBM halt sales of Watson AI tool for drug discovery amid tepid growth: STAT report. Salesforce Einstein team open sources TransmogrifAI, their automated machine learning library How to create sales analysis app in Qlik Sense using DAR method [Tutorial]

Last month, a team of researchers from the University of Washington and the Allen Institute for Artificial Intelligence, published a paper titled ‘Defending Against Neural Fake News’. The goal of this paper is to reliably detect “neural fake news”, so that its harm can be minimized. With this regard, the researchers have built a model named ‘GROVER’. This works as a generator of fake news, which can also spot its own generated fake news articles, as well as those generated by other AI models. GROVER (Generating aRticles by Only Viewing mEtadata Records) models can generate an efficient yet controllable news article, with not only the body, but also the title, news source, publication date, and author list. The researchers affirm that the ‘best models for generating neural disinformation are also the best models at detecting it’. The framework for GROVER represents fake news generation and detection as an adversarial game: Adversary This system will generate fake stories that match specified attributes: generally, being viral or persuasive. The stories must be realistic to read for both human users as well as the verifier. Verifier This system will classify news stories as real or fake. A verifier will have access to unlimited real news stories and few fake news stories from a specific adversary. The dual objective of these two systems suggest an escalating ‘arms race’ between attackers and defenders. It is expected that as the verification systems get better, the adversaries too will follow. Modeling Conditional Generation of Neural Fake News using GROVER GROVER adopts a language modeling framework which allows for flexible decomposition of an article in the order of p(domain, date, authors, headline, body). During inference time, a set of fields are set as ‘F’ for context, with each field ‘f ‘ containing field-specific start and end tokens. During training, the inference is simulated by randomly partitioning an article’s fields into two disjoint sets F1 and F2. The researchers also randomly drop out individual fields with probability 10%, and drop out all but the body with probability 35%. This allows the model to learn how to perform unconditional generation. For Language Modeling, two evaluation modes are considered: unconditional, where no context is provided and the model must generate the article body; and conditional, in which the full metadata is provided as context. The researchers evaluate the quality of disinformation generated by their largest model, GROVER-Mega, using p=.96. The articles are classified into four classes: human-written articles from reputable news websites (Human News), GROVER-written articles conditioned on the same metadata (Machine News), human-written articles from known propaganda websites (Human Propaganda), and GROVER-written articles conditioned on the propaganda metadata (Machine Propaganda). Image Source: Defending Against Neural Fake News When rated by qualified workers on Amazon Mechanical Turk, it was found that though the quality of GROVER-written news is not as high as human-written news, it is very skilled at rewriting propaganda. The overall trustworthiness score of propaganda increases from 2.19 to 2.42 (out of 3) when rewritten by GROVER. Neural Fake News Detection using GROVER The role of the Verifier is to mitigate the harm of neural fake news by classifying articles as Human or Machine written. The neural fake news detection is framed in a semi-supervised method. The neural verifier (or discriminator) will have access to many human-written news articles from March 2019 and before, i.e., the entire RealNews training set. However, it will   have limited access to generations, and more recent news articles. For example, using 10k news articles from April 2019, for generating article body text; another 10k articles are used as a set of human-written news articles, it is split in a balanced way, with 10k for training, 2k for validation, and 8k for testing. It is evaluated using two modes: In the unpaired setting, a verifier is provided single news articles, which must be classified independently as Human or Machine.  In the paired setting, a model is given two news articles with the same metadata, one real and one machine-generated. The verifier must assign the machine-written article a higher Machine probability than the human-written article. Both the modes are evaluated in terms of accuracy. Image Source: Defending Against Neural Fake News It was found that the paired setting appears significantly easier than the unpaired setting across the board, suggesting that it is often difficult for the model to calibrate its predictions. Second, model size is highly important in the arms race between generators and discriminators. Using GROVER to discriminate GROVER’s generations results in roughly 90% accuracy across the range of sizes. If a larger generator is used, accuracy slips below 81%; conversely, if the discriminator is larger, accuracy is above 98%. Lastly, other discriminators perform worse than GROVER overall. This suggests that effective discrimination requires having a similar inductive bias, as the generator. Thus it has been found that GROVER can rewrite propaganda articles, with humans rating the rewritten versions as more trustworthy. At the same time, GROVER can also defend these models. The researchers are of the opinion that an ensemble of deep generative model, such as GROVER should be used to analyze the content of a text. Obviously the working of the GROVER model has caught many people’s attention. https://twitter.com/str_t5/status/1137108356588605440 https://twitter.com/currencyat/status/1137420508092391424 While some are finding this to be an interesting mechanism to combat fake news, others point out that, it doesn't matter if GROVER can identify its own texts, if it can't identify the texts generated by other models. Releasing a model like GROVERcan turn out to be extremely irresponsible rather than defensive. A user on Reddit says that “These techniques for detecting fake news are fundamentally misguided. You cannot just train a statistical model on a bunch of news messages and expect it to be useful in detecting fake news. The reason for this should be obvious: there is no real information about the label ('fake' vs 'real' news) encoded in the data. Whether or not a piece of news is fake or real depends on the state of the external world, which is simply not present in the data. The label is practically independent of the data.” Another user on Hacker News comments that “Generative neural networks these days are both fascinating and depressing - feels like we're finally tapping into how subsets of human thinking & creativity work. But that knocks us off our pedestal, and threatens to make even the creative tasks we thought were strictly a human specialty irrelevant; I know we're a long way off from generalized AI, but we seem to be making rapid progress, and I'm not sure society's mature enough or ready for it. Especially if the cutting edge tools are in the service of AdTech and such, endlessly optimizing how to absorb everybody's spare attention. Perhaps there's some bright future where we all just relax and computers and robots take care of everything for us, but can't help feeling like some part of the human spirit is dying.” Few users feel that this ‘generating and detecting its own fake news’, kind of model is going to be unnecessary in the future. It’s just a matter of time that the text written by algorithms will be exactly similar to a human written text. At that point, there will be no way to distinguish between such articles. A user suggests that “I think to combat fake news, especially algorithmic one, we'll need to innovate around authentication mechanism that can effectively prove who you are and how much effort you put into writing something. Digital signatures or things like that.” For more details about the GROVER model, head over to the research paper. Worried about Deepfakes? Check out the new algorithm that manipulate talking-head videos by altering the transcripts Speech2Face: A neural network that “imagines” faces from hearing voices. Is it too soon to worry about ethnic profiling? OpenAI researchers have developed Sparse Transformers, a neural network which can predict what comes next in a sequence

The Canadian Parliament's Standing Committee on Access to Information, Privacy and Ethics hosted the hearing of the International Grand Committee on Big Data, Privacy and Democracy from Monday, May 27 to Wednesday, May 29.  Witnesses from at least 11 countries appeared before representatives to testify on how governments can protect democracy and citizen rights in the age of big data. This section of the hearing, which took place on May 28, includes Shoshana Zuboff’s take on how to tackle the complexities of surveillance capitalism. She has also provided 21st-century solutions to help tackle the same. Shoshana Zuboff, Author of 'The Age of Surveillance Capitalism', talks about economic imperatives within surveillance capitalism. Zuboff says that the unilateral claiming of private human experience, its translation into behavioral data. These predictions are sold in a new kind of marketplace that trades exclusively in human futures. When we deconstruct the competitive dynamics of these markets we get to understand what the new imperatives are, which are, Scale: as they need a lot of data in order to make good predictions economies of scale; secondly, scope: they need a variety of data to make good predictions. She shared a brief quote from a data scientist, which says, “We can engineer the context around a particular behavior and force change. That way we are learning how to rate the music and then we let the music make them dance.” This behavioral modification is systemically institutionalized on a global scale and mediated by a now ubiquitous digital infrastructure. She further explains the kind of law and regulation needed today will be 21st century solutions aimed at the unique 21st century complexities of surveillance capitalism. She mentioned three arenas in which legislative and regulatory strategies can effectively align with the structure and consequences of surveillance capitalism briefly: We need lawmakers to devise strategies that interrupt and in many cases outlaw surveillance capitalism's foundational mechanisms. This includes the unilateral taking of private human experience as a free source of raw material and its translation into data. It includes the extreme information asymmetries necessary for predicting human behavior. It includes the manufacture of computational prediction products based on the unilateral and secret capture of human experience. It includes the operation of prediction markets that trade in human futures. From the point of view of supply and demand, surveillance capitalism can be understood as a market failure. Every piece of research over the last decades has shown that when users are informed of the backstage operations of surveillance capitalism they want no part of it, they want protection, they reject it, they want alternatives. We need laws and regulatory frameworks designed to advantage companies that want to break with the surveillance capitalist paradigm. Forging an alternative trajectory to the digital future will require alliances of new competitors who can summon and institutionalize an alternative ecosystem. True competitors that align themselves with the actual needs of people and the norms of market democracy are likely to attract just about every person on earth as their customers. Lawmakers will need to support new forms of citizen action, collective action just as nearly a century ago workers won legal protection for their rights to organize to bargain and to and to strike. New forms of citizen solidarity are already emerging in municipalities that seek an alternative to the Google-owned Smart City future. In communities that want to resist the social cost of so-called disruption imposed for the sake of others gained and among workers who seek fair wages and reasonable security in the precarious conditions of the so-called gig economy. She says, “Citizens need your help but you need citizens because ultimately they will be the wind behind your wings, they will be the sea change in public opinion and public awareness that supports your political initiatives.” “If together we aim to shift the trajectory of the digital future back toward its emancipatory promise, we resurrect the possibility that the future can be a place that all of us might call home,” she concludes. To know more you can listen to the full hearing video titled, “Meeting No. 152 ETHI - Standing Committee on Access to Information, Privacy and Ethics” on ParlVU. WWDC 2019 highlights: Apple introduces SwiftUI, new privacy-focused sign in, updates to iOS, macOS, and iPad and more Experts present most pressing issues facing global lawmakers on citizens’ privacy, democracy and rights to freedom of speech Apple previews iOS 13: Sign in with Apple, dark mode, advanced photo and camera features, and an all-new Maps experience  

Wouldn’t it be magical if we could watch old black and white movie footages and images in color? Deep learning, more precisely, GANs can help here. A recent approach by a software researcher Jason Antic tagged as ‘DeOldify’ is a deep learning based project for colorizing and restoring old images and film footages. https://twitter.com/johnbreslin/status/1127690102560448513 https://twitter.com/johnbreslin/status/1129360541955366913 In one of the sessions at the recent Facebook Developer Conference held from April 30 - May 1, 2019, Antic, along with Jeremy Howard, and Uri Manor talked about how by using GANs one can reconstruct images and videos, such as increasing their resolution or adding color to a black and white film. However, they also pointed out that GANs can be slow, and difficult and expensive to train. They demonstrated how to colorize old black & white movies and drastically increase the resolution of microscopy images using new PyTorch-based tools from fast.ai, the Salk Institute, and DeOldify that can be trained in just a few hours on a single GPU. https://twitter.com/citnaj/status/1123748626965114880 DeOldify makes use of a NoGAN training, which combines the benefits of GAN training (wonderful colorization) while eliminating the nasty side effects (like flickering objects in the video). NoGAN training is crucial while getting some images or videos stable and colorful. An example of DeOldify trying to achieve a stable video is as follows: Source: GitHub Antic said, “the video is rendered using isolated image generation without any sort of temporal modeling tacked on. The process performs 30-60 minutes of the GAN portion of "NoGAN" training, using 1% to 3% of Imagenet data once. Then, as with still image colorization, we "DeOldify" individual frames before rebuilding the video.” The three models in DeOldify DeOldify includes three models including video, stable and artistic. Each of the models has its strengths and weaknesses, and their own use cases. The Video model is for video and the other two are for images. Stable https://twitter.com/johnbreslin/status/1126733668347564034 This model achieves the best results with landscapes and portraits and produces fewer zombies (where faces or limbs stay gray rather than being colored in properly). It generally has less unusual miscolorations than artistic, but it's also less colorful in general. This model uses a resnet101 backbone on a UNet with an emphasis on width of layers on the decoder side. This model was trained with 3 critic pretrain/GAN cycle repeats via NoGAN, in addition to the initial generator/critic pretrain/GAN NoGAN training, at 192px. This adds up to a total of 7% of Imagenet data trained once (3 hours of direct GAN training). Artistic https://twitter.com/johnbreslin/status/1129364635730272256 This model achieves the highest quality results in image coloration, with respect to interesting details and vibrance. However, in order to achieve this, one has to adjust the rendering resolution or render_factor. Additionally, the model does not do as well as ‘stable’ in a few key common scenarios- nature scenes and portraits. Artistic model uses a resnet34 backbone on a UNet with an emphasis on depth of layers on the decoder side. This model was trained with 5 critic pretrain/GAN cycle repeats via NoGAN, in addition to the initial generator/critic pretrain/GAN NoGAN training, at 192px. This adds up to a total of 32% of Imagenet data trained once (12.5 hours of direct GAN training). Video https://twitter.com/citnaj/status/1124719757997907968 The Video model is optimized for smooth, consistent and flicker-free video. This would definitely be the least colorful of the three models; while being almost close to the ‘stable’ model. In terms of architecture, this model is the same as "stable"; however, differs in training. It's trained for a mere 2.2% of Imagenet data once at 192px, using only the initial generator/critic pretrain/GAN NoGAN training (1 hour of direct GAN training). DeOldify was achieved by combining certain approaches including: Self-Attention Generative Adversarial Network: Here, Antic has modified the generator, a pre-trained U-Net, to have the spectral normalization and self-attention. Two Time-Scale Update Rule: It’s just one to one generator/critic iterations and higher critic learning rate. This is modified to incorporate a "threshold" critic loss that makes sure that the critic is "caught up" before moving on to generator training. This is particularly useful for the "NoGAN" method. NoGAN doesn’t have a separate research paper. This, in fact, is a new type of GAN training developed to solve some key problems in the previous DeOldify model. NoGAN includes the benefits of GAN training while spending minimal time doing direct GAN training. Antic says, “I'm looking to make old photos and film look reeeeaaally good with GANs, and more importantly, make the project useful.” “I'll be actively updating and improving the code over the foreseeable future. I'll try to make this as user-friendly as possible, but I'm sure there's going to be hiccups along the way”, he further added. To further know about the hardware components and other details head over to Jason Antic’s GitHub page. Training Deep Convolutional GANs to generate Anime Characters [Tutorial] Sherin Thomas explains how to build a pipeline in PyTorch for deep learning workflows Using deep learning methods to detect malware in Android Applications

The use of AI (Artificial Intelligence) technology in commercial aviation has brought some significant changes in the ways flights are being operated today. World’s leading airliner service providers are now using AI tools and technologies to deliver a more personalized traveling experience to their customers. From building AI-powered airport kiosks to using it for automating airline operations and security checking, AI will play even more critical roles in the aviation industry. Engineers have found AI can help the aviation industry with machine vision, machine learning, robotics, and natural language processing. Artificial intelligence has been found to be highly potent and various researches have shown how the use of artificial intelligence can bring significant changes in aviation. Few airlines now use artificial intelligence for predictive analytics, pattern recognition, auto scheduling, targeted advertising, and customer feedback analysis showing promising results for better flight experience. A recent report shows that aviation professionals are thinking to use artificial intelligence to monitor pilot voices for a hassle-free flying experience of the passengers. This technology is to bring huge changes in the world of aviation. Identification of the Passengers There’s no need to explain how modern inventions are contributing towards the betterment of mankind and AI can help in air transportation in numerous ways. Check-in before boarding is a vital task for an airline and they can simply take the help of artificial intelligence to do it easily, the same technology can be also used for identifying the passengers as well. American airline company Delta Airlines took the initiative in 2017. Their online check-in via Delta mobile app and ticketing kiosks have shown promising results and nowadays you can see many airlines taking similar features to the whole new level. The Transportation Security Administration of the United States has introduced new AI technology to identify potential threats at the John F. Kennedy, Los Angeles International Airport and Phoenix airports. Likewise, Hartsfield-Jackson Airport is planning to launch America’s first biometric terminal. Once installed, “the AI technology will make the process of passenger identification fast and easy for officials. Security scanners, biometric identification”, and machine learning are some of the AI technologies that will make a number of jobs easy for us. In this way, AI helps us predict disruption in airline services. Baggage Screening Baggage screening is another tedious but important task that needs to be done at the airport. However, AI has simplified the process of baggage screening. The American airlines once conducted a competition on app development on artificial intelligence and Team Avatar became the winner of the competition for making an app that would allow the users to determine the size of their baggage at the airport. Osaka Airport in Japan is planning to install the Syntech ONE 200, which is an AI technology developed to screen baggage for multiple passenger lanes. Such tools will not only automate the process of baggage screening but also help authorities detect illegal items effectively. Syntech ONE 200is compatible with the X-ray security system and it increases the probability of identification of potential threats. Assisting Customers AI can be used to assist customers in the airport and it can help a company reduce its operational costs and labor costs at the same time. Airlines companies are now using AI technologies to help their customers to resolve issues quickly by getting accurate information on future flights trips on their internet-enabled devices. More than 52% of airlines companies across the world have planned to install AI-based tools to improve their customer service functions in the next five years. Artificial Intelligence can answer various common questions of the customers, assisting them for check-in requests, the status of the flight and more. Nowadays artificial intelligence is also used in air cargo for different purposes such as revenue management, safety, and maintenance and it has shown impressive results till date. Maintenance Prediction Airlines companies are planning to implement AI technology to predict potential failures of maintenance on aircraft. Leading aircraft manufacturer Airbus is taking measures to improve the reliability of aircraft maintenance. They are using Skywise, a cloud-based data storing system. It helps the fleet to collect and record a huge amount of real-time data. The use of AI in the predictive maintenance analytics will pave the way for a systematic approach on how and when the aircraft maintenance should be done.  Nowadays you can see how top-rated airlines use artificial intelligence to make the process of maintenance easy and improve the user experience at the same time. Pitfalls of using AI in Aviation Despite being considered as a future of the aviation industry,  AI has some pitfalls. For instance, it takes time for implementation and it cannot be used as an ideal tool for customer service. The recent incident of Ethiopian Airlines Boeing 737 was an eye-opener for us and it clearly represents the drawback of AI technology in the aviation sector. The Boeing 737 crashed a few minutes after it took off from the capital of Ethiopia. The failure of the MCAC system was the key reasons behind the fatal accident. Also, AI is quite expensive; for example, if an airline company is planning to deploy a chatbot, it will have to invest more than $15,000. Thus, it would be a hard thing for small companies to invest for the same and this could create a barrier between small and big airlines in the future. As the market is becoming highly competitive, big airlines will conquer the market and the small airlines might face an existential threat due to this reason.   Conclusion The use of artificial intelligence in aviation has made many tasks easy for airlines and airport authorities across the world. From identifying passengers to screening the bags and providing fast and efficient customer care solutions. Unlike the software industry, the risks of real life harms are exponentially higher in the aviation industry. While other industries have started using this technology long back, the adoption of AI in aviation has been one of caution, and rightly so. As the aviation industry embraces the benefits of artificial intelligence and machine learning, it must also invest in putting in place checks and balances to identify, reduce and eliminate harmful consequences of AI, whether intended or otherwise.  As Silicon Valley reels in ethical dilemmas, the aviation industry will do well to learn from Silicon Valley while making a transition to a smart future. The aviation industry known for its rigorous safety measures and processes may, in fact, have a thing or two to teach Silicon Valley when it comes to designing, adopting and deploying AI systems into live systems that have high-risk profiles. Author Bio Maria Brown is Content Writer, Blogger and maintaining Social Media Optimization for 21Twelve Interactive. She believes in sharing her solid knowledge base with a focus on entrepreneurship and business. You can find her on Twitter.

On the second day of NeurIPS conference held in Montreal, Canada last year, Dr. Joelle Pineau presented a talk on reproducibility in reinforcement learning. She is an Associate Professor at McGill University and Research Scientist for Facebook, Montreal, and the talk is ‘Reproducible, Reusable, and Robust Reinforcement Learning’. Reproducibility and crisis Dr. Pineau starts by stating a quote from Bollen et. al in National Science Foundation: “Reproducibility refers to the ability of a researcher to duplicate the results of a prior study, using the same materials as were used by the original investigator. Reproducibility is a minimum necessary condition for a finding to be believable and informative.” Reproducibility is not a new concept and has appeared across various fields. In a 2016 The Nature journal survey of 1576 scientists, 52% said that there is a significant reproducibility crisis, 38% agreed to a slight crisis. Reinforcement learning is a very general framework for decision making. About 20,000 papers are published in this area alone in 2018 and the year is not even over yet, compared to just about 2,000 papers in the year 2000. The focus of the talk is a class of reinforcement learning that has gotten the most attention and has shown a lot of promise for practical applications—policy gradients. In this method, the idea is that the policy/strategy is learned as a function and this function can be represented by a neural network. Pineau picks four research papers in the class of policy gradients that come across literature most often. They use the Mujocu simulator to compare the four algorithms. It is not important to know which algorithm is which but the approach to empirically compare these algorithms is the intention. The results were different in different environments (Hopper, Swimmer) but the variance was also drastically different for an algorithm. Even on using different code and policies the results were very different for a given algorithm in different environments. It was observed that people writing papers may not be always motivated to find the best possible hyperparameters and very often use the default hyperparameters. On using the best hyperparameters possible for two algorithms compared fairly, the results were pretty clean, distinguishable. Where n=5, five different random seeds. Picking n influences the size of the confidence interval (CI). n=5 here as most papers used 5 trials at the most. Some people were also run “n” runs where n was not specified and would report the top 5 results. It is a good way to show good results but there’s a strong positive bias, the variance appears to be small. Source: NeurIPS website Some people argue that the field of reinforcement learning is broken. Pineau stresses that this is not her message and notes that sometimes fair comparisons don’t have to give the cleanest results. Different methods may have a very distinct set of hyperparameters in number, value, and variable sensitivity. Most importantly the best method to choose heavily depends on the data and computation budget you can spare. An important point to get the said reproducibility when using algorithms to your problem. Pineau and her team surveyed 50 RL papers from 2018 and found that significance testing was applied only on 5% of the papers. Graphs and shading is seen in many papers but without information on what the shading area is, confidence interval or standard deviation cannot be known. Pineau says: “Shading is good but shading is not knowledge unless you define it properly.” A reproducibility checklist For people publishing papers Pineau presents a checklist created in consultation with her colleagues. It says for algorithms the things included should be a clear description, an analysis of complexity, and a link to source code and dependencies. For theoretical claims, a statement of the result, a clear explanation of any assumptions, and a complete proof of the claim should be included. There are also other items presented in the checklist for figures and tables. Here is the complete checklist: Source: NeurIPS website Role of infrastructure on reproducibility People can think that since the experiments are run on computers results will be more predictable than those of other sciences. But even in hardware, there is room for variability. Hence, specifying it can be useful. For example the properties of CUDA operations. On some myths “Reinforcement Learning is the only case of ML where it is acceptable to test on your training set.” Do you have to train and test on the same task? Pineau says that you really don’t have to after presenting three examples. The first one is where the agent moves around in four directions on an image then identifies what the image is, on higher n, the variance is greatly reduced. The second one is of an Atari game where the black background is replaced with videos which are a source of noise, a better representation of the real world as compared to a simulated limited environment where external real-world factors are not present. She then talks about multi-task RL in photorealistic simulators to incorporate noise. The simulator is an emulator built from images videos taken from real homes. Environments created are completely photorealistic but have properties of the real world, for example, mirror reflection. Working in the real world is very different than a limited simulation. For one, a lot more data is required to represent the real world as compared to a simulation. The talk ends with a message that science is not a competitive sport but is a collective institution that aims to understand and explain. There is an ICLR reproducibility challenge where you can join. The goal is to get community members to try and reproduce the empirical results presented in a paper, it is on an open review basis. Last year, 80% changed their paper with the feedback given by contributors who tested a given paper. Head over to NeurIPS facebook page for the entire lecture and other sessions from the conference. How NeurIPS 2018 is taking on its diversity and inclusion challenges NeurIPS 2018: Rethinking transparency and accountability in machine learning Researchers unveil a new algorithm that allows analyzing high-dimensional data sets more effectively, at NeurIPS conference

As the number of potential borrowers continues to rapidly grow, loan companies and banks are having a bad time trying to figure out how likely their customers are to pay back. Probably, getting information on clients’ creditworthiness is the greatest challenge for most financial companies, and it especially concerns those clients who don’t have any credit history yet. There is no denying that the alternative lending business has become one of the most influential financial branches both in the USA and Europe. Debt is a huge business of our days that needs a lot of resources. In such a challenging situation, any means that can improve productivity and reduce the risk of mistake while performing financial activities are warmly welcomed. This is actually how Artificial Intelligence became the redemption for loan providers. Fortunately for lenders, AI successfully deals with this task by following the borrowers’ digital footprint. For example, some applications for digital lending collect and analyze an individual’s web browsing history (upon receiving their personal agreement on the use of this information). In some countries such as China and Africa, they may also look through their social network profiles, geolocation data, and the messages sent to friends and family, counting the number of punctuation mistakes. The collected information helps loan providers make the right decision on their clients’ creditworthiness and avoid long loan processes. When AI Overfits Unfortunately, there is the other side of the coin. There’s a theory which states that people who pay for their gas inside the petrol station, not at the pump, are usually smokers. And that is the group whose creditworthiness is estimated to be low. But what if this poor guy simply wanted to buy a Snickers? This example shows that if a lender leaves without checking the information carefully gathered by AI software, they may easily end up with making bad mistakes and misinterpretations. Artificial Intelligence in the financial sector may significantly reduce costs, efforts, and further financial complications, but there are hidden social costs such as the above. A robust analysis, design, implementation and feedback framework is necessary to meaningfully counter AI bias. Other Use Cases for AI in Finances Of course, there are also enough examples of how AI helps to improve customer experience in the financial sector. Some startups use AI software to help clients find the company that is the best at providing them with the required service. They juxtapose the clients’ requirements with the companies’ services finding perfect matches. Even though this technology reminds us of how dating apps work, such applications can drastically save time for both parties and help borrowers pay faster. AI can also be used for streamlining finances. AI helps banks and alternative lending companies in automating some of their working processes such as basic customer service, contract management, or transactions monitoring. A good example is Upstart, the pet project of two former Google employees. The startup was originally aimed to help young people lacking the credit history, to get a loan or any other kind of financial support. For this purpose, the company uses the clients’ educational background and experience, taking into account things such as their attained degrees and school/university attendance. However, such approach to lending may end up being a little snobbish: it can simply overlook large groups of population who can’t afford higher education. As a result of insufficient educational background, these people can become deprived of the opportunity to get their loan. Nonetheless, one of the main goals of the company was automating as many of its operating procedures as possible. By 2018, more than 60% of all their loans had been fully automated with more to come. We cannot automate fairness and opportunity, yet The implementation of machine learning in providing loans by checking the digital footprint of people may lead to ethical and legal disputes. Even today some people state that the use of AI in the financial sector encouraged inequality in the number of loans provided to the black and white population of the USA. They believe that AI continues the bias against minorities and make the black people “underbanked.” Both lending companies and banks should remember that the quality of work done these days with the help of machine learning methods highly depends on people—both employees who use the software and AI developers who create and fine-tune it. So we should see AI in loan management as a useful tool—but not as a replacement for humans. Author Bio Darya Shmat is a business development representative at Iflexion, where Darya expertly applies 10+ years of practical experience to help banking and financial industry clients find the right development or QA solution. Blockchain governance and uses beyond finance – Carnegie Mellon university podcast Why Retailers need to prioritize eCommerce Automation in 2019 Glancing at the Fintech growth story – Powered by ML, AI & APIs

In 2017, we predicted that artificial general intelligence will gain traction in research and certain areas will aid towards AGI systems. The prediction was made in a set of other AI predictions in an article titled 18 striking AI Trends to watch in 2018. Let’s see how 2018 went for AGI research. Artificial general intelligence or AGI is an area of AI in which efforts are made to make machines have intelligence closer to the complex nature of human intelligence. Such a system could possibly, in theory, perform tasks that a human can with the ability to learn as it progresses through tasks, collects data/sensory input. Human intelligence also involves learning a skill and applying it to other areas. For example, if a human learns Dota 2, they can apply the same learned experience to other similar strategy games, only the UI and characters in the game that can be adopted will be different. A machine cannot do this, AI systems are trained for a specific area and the skills cannot really be transferred to another task with complete efficiency and the fear of causing technical debt. That is, a machine cannot generalize skills as a human can. Come 2018, we saw Deepmind’s AlphaZero, something that is at least beginning to show what an idea of AGI could look like. But even this is not really AGI, an AlphaZero like system may excel at playing a variety of games or even understand the rules of novel games but cannot deal with the real world and its challenges. Some groundwork and basic ideas for AGI were set in a paper by the US Air Force. Dr. Paul Yaworsky, in the paper, says that artificial general intelligence is an effort to cover the gap between lower and higher level work in AI. So to speak, try and make sense of the abstract nature of intelligence. The paper also shows an organized hierarchical model for intelligence considering the external world. One of Packt’s authors, Sudharsan Ravichandiran thinks that: “Great things are happening around RL research each and every day. Deep Meta reinforcement learning will be the future of AI where we will be so close to achieving artificial general intelligence (AGI). Instead of creating different models to perform different tasks, with AGI, a single model can master a wide variety of tasks and mimics the human intelligence.” Honda came up with a program called Curious Minded Machine in association with MIT, University of Pennsylvania, and the University of Washington. The idea sounds simple at first - it is to build a model on how children ‘learn to learn’. But something like this which children do instinctively is a very complex task for a machine/computer with artificial intelligence. The teams will showcase their work in various fields they are working on at the end of three years since the inception of the program. There was another effort by SingularityNET and Mindfire to explore AI and “cracking the brain code”. The effort is to better understand the functioning of the human brain. Together these two companies will focus on three key areas—talent, AI services, and AI education. Mindfire Mission 2 will take place in early 2019, Switzerland. These were the areas of work we saw on AGI in 2018. There were only small steps taken towards the research direction and nothing noteworthy that gained mainstream traction. On an average, experts think AGI would take at least a 100 more years to be a reality, as per Martin Ford’s interviews with machine learning experts for his best selling book, ‘Architects of Intelligence’. OpenAI released a new language model called GPT-2 in February 2019. With just one line of words, the model can generate whole articles. The results are good enough to pass as something written by a human. This does not mean that the machine actually understands human language, it’s merely generating sentences by associating words. This development has triggered passionate discussions within the community on not just the technical merits of the findings, but also the dangers and implications of applications of such research on the larger society. Get ready to see more tangible research in AGI in the next few decades. The US Air Force lays groundwork towards artificial general intelligence based on hierarchical model of intelligence Facebook’s artificial intelligence research team, FAIR, turns five. But what are its biggest accomplishments? Unity and Deepmind partner to develop Virtual worlds for advancing Artificial Intelligence

The biggest challenge for anyone working in tech is that you need multiple sets of eyes. Yes, you need to commit to regular, almost continuous learning, but you also need to look forward to what’s coming next. From slowly emerging trends that might not even come to fruition (we’re looking at you DataOps), to version updates and product releases, for tech professionals the horizon always looms and shapes the present. But it’s not just about the big trends or releases that get coverage - it’s also about planning your next (career) move, or even your next mini-project. That could be learning a new language (not necessarily new, but one you haven’t yet got round to learning), trying a new paradigm, exploring a new library, or getting to grips with cloud native approaches to software development. This sort of learning is easy to overlook but it is one that's vital to any developers' development. While the Packt library has a wealth of content for you to dig your proverbial claws into, if you’re looking forward, Packt has got some new titles available in pre-order that could help you plan your learning for the months to come. We’ve put together a list of some of our own top picks of our pre-order titles available this month, due to be released late February or March. Take a look and take some time to consider your next learning journey... Hands-on deep learning with PyTorch TensorFlow might have set the pace when it comes to artificial intelligence, but PyTorch is giving it a run for its money. It’s impossible to describe one as ‘better’ than the other - ultimately they both have valid use cases, and can both help you do some pretty impressive things with data. Read next: Can a production ready Pytorch 1.0 give TensorFlow a tough time? The key difference is really in the level of abstraction and the learning curve - TensorFlow is more like a library, which gives you more control, but also makes things a little more difficult. PyTorch, then, is a great place to start if you already know some Python and want to try your hand at deep learning. Or, if you have already worked with TensorFlow and simply want to explore new options, PyTorch is the obvious next step. Order Hands On Deep learning with PyTorch here. Hands-on DevOps for Architects Distributed systems have made the software architect role incredibly valuable. This person is not only responsible for deciding what should be developed and deployed, but also the means through which it should be done and maintained. But it’s also made the question of architecture relevant to just about everyone that builds and manages software. That’s why Hands on DevOps for Architects is such an important book for 2019. It isn’t just for those who typically describe themselves as software architects - it’s for anyone interested in infrastructure, and how things are put together, and be made to be more reliable, scalable and secure. With site reliability engineering finding increasing usage outside of Silicon Valley, this book could be an important piece in the next step in your career. Order Hands-on DevOps for Architects here. Hands-on Full stack development with Go Go has been cursed with a hell of a lot of hype. This is a shame - it means it’s easy to dismiss as a fad or fashion that will quickly disappear. In truth, Go’s popularity is only going to grow as more people experience, its speed and flexibility. Indeed, in today’s full-stack, cloud native world, Go is only going to go from strength to strength. In Hands-on Full Stack Development with Go you’ll not only get to grips with the fundamentals of Go, you’ll also learn how to build a complete full stack application built on microservices, using tools such as Gin and ReactJS. Order Hands-on Full Stack Development with Go here. C++ Fundamentals C++ is a language that often gets a bad rap. You don’t have to search the internet that deeply to find someone telling you that there’s no point learning C++ right now. And while it’s true that C++ might not be as eye-catching as languages like, say, Go or Rust, it’s nevertheless still a language that still plays a very important role in the software engineering landscape. If you want to build performance intensive apps for desktop C++ is likely going to be your go-to language. Read next: Will Rust replace C++? One of the sticks that’s often used to beat C++ is that it’s a fairly complex language to learn. But rather than being a reason not to learn it, if anything the challenge it presents to even relatively experienced developers is one well worth taking on. At a time when many aspects of software development seem to be getting easier, as new layers of abstraction remove problems we previously might have had to contend with, C++ bucks that trend, forcing you to take a very different approach. And although this approach might not be one many developers want to face, if you want to strengthen your skillset, C++ could certainly be a valuable language to learn. The stats don’t lie - C++ is placed 4th on the TIOBE index (as of February 2019), beating JavaScript, and commands a considerably high salary - indeed.com data from 2018 suggests that C++ was the second highest earning programming language in the U.S., after Python, with a salary of $115K. If you want to give C++ a serious go, then C++ Fundamentals could be a great place to begin. Order C++ Fundamentals here. Data Wrangling with Python & Data Visualization with Python Finally, we’re grouping two books together - Data Wrangling with Python and Data Visualization with Python. This is because they both help you to really dig deep into Python’s power, and better understand how it has grown to become the definitive language of data. Of course, R might have something to say about this - but it’s a fact the over the last 12-18 months Python has really grown in popularity in a way that R has been unable to match. So, if you’re new to any aspect of the data science and analysis pipeline, or you’ve used R and you’re now looking for a faster, more flexible alternative, both titles could offer you the insight and guidance you need. Order Data Wrangling with Python here. Order Data Visualization with Python here.